Visual Question Answering (VQA)

Visual Question Answering (VQA) is a task in computer vision that involves answering questions about an image. The goal of VQA is to teach machines to understand the content of an image and answer questions about it in natural language.

Image Source: visualqa.org

Papers

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Showing 1511–1520 of 2167 papers

Title	Date	Tasks	Status	Hype
Generating and Evaluating Explanations of Attended and Error-Inducing Input Regions for VQA Models	Mar 26, 2021	Question AnsweringVisual Question Answering	—Unverified	0
On the hidden treasure of dialog in video question answering	Mar 26, 2021	Question AnsweringVideo Question Answering	CodeCode Available	1
Visual Grounding Strategies for Text-Only Natural Language Processing	Mar 25, 2021	Image RetrievalLanguage Modeling	—Unverified	0
Multi-Modal Answer Validation for Knowledge-Based VQA	Mar 23, 2021	Question AnsweringRetrieval	CodeCode Available	1
How to Design Sample and Computationally Efficient VQA Models	Mar 22, 2021	Question AnsweringVisual Question Answering	—Unverified	0
A Comprehensive Survey of Scene Graphs: Generation and Application	Mar 17, 2021	Image CaptioningQuestion Answering	—Unverified	0
Automatic Generation of Contrast Sets from Scene Graphs: Probing the Compositional Consistency of GQA	Mar 17, 2021	Question AnsweringRelational Reasoning	CodeCode Available	0
VMAF And Variants: Towards A Unified VQA	Mar 13, 2021	feature selectionregression	—Unverified	0
Characterizing Misclassifications of Deep NLP Models	Mar 12, 2021	named-entity-recognitionNamed Entity Recognition	—Unverified	0
RL-CSDia: Representation Learning of Computer Science Diagrams	Mar 10, 2021	Question AnsweringRepresentation Learning	—Unverified	0

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All datasets GQA Test2019 VQA v2 test-dev VQA v2 test-std OK-VQA MSVD-QA DocVQA test MSRVTT-QA InfographicVQA GQA test-dev VizWiz 2020 VQA A-OKVQA CLEVR

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	human	Accuracy	89.3	—	Unverified
2	DREAM+Unicoder-VL (MSRA)	Accuracy	76.04	—	Unverified
3	TRRNet (Ensemble)	Accuracy	74.03	—	Unverified
4	MIL-nbgao	Accuracy	73.81	—	Unverified
5	Kakao Brain	Accuracy	73.33	—	Unverified
6	Coarse-to-Fine Reasoning, Single Model	Accuracy	72.14	—	Unverified
7	270	Accuracy	70.23	—	Unverified
8	NSM ensemble (updated)	Accuracy	67.55	—	Unverified
9	VinVL-DPT	Accuracy	64.92	—	Unverified
10	VinVL+L	Accuracy	64.85	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	PaLI	Accuracy	84.3	—	Unverified
2	BEiT-3	Accuracy	84.19	—	Unverified
3	VLMo	Accuracy	82.78	—	Unverified
4	ONE-PEACE	Accuracy	82.6	—	Unverified
5	mPLUG (Huge)	Accuracy	82.43	—	Unverified
6	CuMo-7B	Accuracy	82.2	—	Unverified
7	X2-VLM (large)	Accuracy	81.9	—	Unverified
8	MMU	Accuracy	81.26	—	Unverified
9	Lyrics	Accuracy	81.2	—	Unverified
10	InternVL-C	Accuracy	81.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BEiT-3	overall	84.03	—	Unverified
2	mPLUG-Huge	overall	83.62	—	Unverified
3	ONE-PEACE	overall	82.52	—	Unverified
4	X2-VLM (large)	overall	81.8	—	Unverified
5	VLMo	overall	81.3	—	Unverified
6	SimVLM	overall	80.34	—	Unverified
7	X2-VLM (base)	overall	80.2	—	Unverified
8	VAST	overall	80.19	—	Unverified
9	VALOR	overall	78.62	—	Unverified
10	Prompt Tuning	overall	78.53	—	Unverified